Integration of nanoscale memristor synapses in neuromorphic computing architectures
Giacomo Indiveri;Bernabé Linares-Barranco;Robert A. Legenstein;George Deligeorgis.
Nanotechnology (2013)
612 Citations
2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models
Robert Legenstein;Wolfgang Maass.
Neural Networks (2007)
489 Citations
Combining predictions for accurate recommender systems
Michael Jahrer;Andreas Töscher;Robert Legenstein.
knowledge discovery and data mining (2010)
337 Citations
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback
Robert A. Legenstein;Dejan Pecevski;Wolfgang Maass.
PLOS Computational Biology (2008)
292 Citations
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses.
Alexander Serb;Johannes Bill;Ali Khiat;Radu Berdan.
Nature Communications (2016)
282 Citations
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Robert Legenstein;Christian Naeger;Wolfgang Maass.
Neural Computation (2005)
272 Citations
Long short-term memory and Learning-to-learn in networks of spiking neurons
Guillaume Emmanuel Fernand Bellec;Darjan Salaj;Anand Subramoney;Robert Legenstein.
neural information processing systems (2018)
186 Citations
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons
Lars Büsing;Benjamin Schrauwen;Robert Legenstein.
Neural Computation (2010)
168 Citations
A solution to the learning dilemma for recurrent networks of spiking neurons
Guillaume Emmanuel Fernand Bellec;Franz Scherr;Anand Subramoney;Elias Hajek.
Nature Communications (2020)
156 Citations
Branch-Specific Plasticity Enables Self-Organization of Nonlinear Computation in Single Neurons
Robert Legenstein;Wolfgang Maass.
The Journal of Neuroscience (2011)
154 Citations
